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AI-Powered OpenTelemetry Analysis MCP Server

@shiftyp

リポジトリ

基本情報

カテゴリ

開発者ツール

ライセンス

MIT

トランスポート

stdio

作者

shiftyp

AI-Powered OpenTelemetry Analysis MCP Server について

概要はまだありません

#otel-mcp-server#observability#ai-assistant

AI-Powered OpenTelemetry Analysis MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "otel-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "otel-mcp-server"
      ],
      "env": {
        "OPENSEARCH_URL": "http://localhost:9200",
        "USERNAME": "elastic",
        "PASSWORD": "changeme",
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

AI-Powered OpenTelemetry Analysis MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is AI-Powered OpenTelemetry Analysis?

This MCP server bridges AI assistants with OpenTelemetry data stored in Elasticsearch/OpenSearch, enabling natural language queries on traces, metrics, and logs. It is for developers, SREs, and anyone needing conversational access to observability data without writing query languages.

How to use AI-Powered OpenTelemetry Analysis?

Add the server to your MCP settings for Windsurf/Claude Desktop using environment variables like OPENSEARCH_URL, USERNAME, PASSWORD, and optionally OPENAI_API_KEY. For developers, clone the repo, install dependencies, configure .env, build, and integrate with your MCP client via a direct node command to dist/server.js.

Key features of AI-Powered OpenTelemetry Analysis

  • Natural language querying of traces, metrics, and logs.
  • Automatic cross-signal correlation and pattern recognition.
  • Anomaly detection and service dependency mapping.
  • Error propagation tracing through distributed systems.
  • Time series analysis with trend and seasonality detection.

Use cases of AI-Powered OpenTelemetry Analysis

  • Instant incident response by investigating error patterns in natural language.
  • Proactive anomaly detection without static alerts.
  • Democratized observability for team members without query expertise.
  • Context-aware development by checking production behavior during code review.
  • Performance analysis like identifying slow operations or comparing baselines.

よくある質問

1

What OpenTelemetry data does this server work with?

It works with traces, metrics, and logs—the three pillars of OpenTelemetry—stored in Elasticsearch or OpenSearch.

2

What are the runtime requirements?

You need either `ELASTICSEARCH_URL` or `OPENSEARCH_URL` with credentials, and optionally an `OPENAI_API_KEY` for ML-powered features. The server runs via `npx` or a local Node.js build.

3

How does the AI correlate data across signals?

The AI automatically correlates traces, metrics, and logs when answering questions, providing context such as error propagation, latency patterns, and service dependencies.

4

What transports and authentication are supported?

The server uses standard MCP configuration with environment variable-based authentication (`USERNAME`, `PASSWORD`) for the data store. No other transports are mentioned in the README.

5

Are there any known limitations?

The README does not list explicit known limits; however, it notes that data must be in Elasticsearch/OpenSearch, and it requires an AI assistant (MCP client) to interpret natural language.

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